Rethinking Red Teaming for AI: The new wave of Cybersecurity in the age of AI

I remember working with a colleague on a complex security project. We were tasked with stress-testing a new AI model for a company. It wasn’t just about protecting data but also ensuring the AI could make accurate, High-stakes decisions in real time.

We started with a traditional approach, analyzing the system’s code and searching for technical flaws, but something wasn’t adding up. No matter how hard we probed, the robustness of the model held steady, and nothing seemed to break. After hours of frustration, I said to my colleague (a Pen Tester) something that had suddenly dawned on me, something completely out of left field:

“Stop thinking like a hacker. Think like the AI.”

At first, he thought I was joking, but then he realized what I meant. We were trying to force the system to fail based on our understanding of security, but AI doesn’t operate like traditional software. It learns, adapts, and interprets data in ways we couldn’t anticipate through code alone. It's inputs needed to be capable of tricking the model through the use of cleverly crafted inputs much like social engineering but with a twist.

So, we changed our strategy. We began to test the AI’s decision-making processes, feeding it scenarios that confused its logic or challenged its cognitive biases. Suddenly, cracks started to appear—not in the system’s code, but in the way it handled ambiguous data and ethical dilemmas. The AI struggled to weigh conflicting inputs and made decisions that would have been disastrous in....

Author

John Vaina

Cybersecurity professional with expertise in Adversarial AI Red Teaming and meta prompt engineering, focusing on large language models (LLMs) and AI/ML security. He has developed groundbreaking adversarial techniques and strategies, merging cognitive science, ethical hacking, and cybersecurity to explore AI vulnerabilities. John is a leading voice in interdisciplinary approaches to AI and machine learning, challenging conventional cybersecurity methods, creating cutting-edge AI defenses, and pushing the boundaries of AI security research. His work blends innovation with technical mastery to secure the future of AI systems.

John is also the author of the "Polyrhythmic-Reasoning Prompt Pattern," an innovative technique for advanced AI interactions. He can be found advancing AI research, writing on cybersecurity, and contributing to industry-leading AI red teaming projects through bleeding-edge AI/ML security research.